Fit Options

The optional [fit_options] block configures the estimation method and optimizer settings.

Syntax

[fit_options]
  key = value

General Options

Key	Values	Default	Description
`method`	`foce`, `focei`, `saem`	`foce`	Estimation method
`maxiter`	integer	`500`	Maximum outer loop iterations
`covariance`	`true`, `false`	`true`	Compute covariance matrix and standard errors
`optimizer`	`slsqp`, `lbfgs`, `nlopt_lbfgs`, `mma`, `bfgs`	`slsqp`	Optimization algorithm
`global_search`	`true`, `false`	`false`	Run gradient-free pre-search before local optimization
`global_maxeval`	integer	auto	Max evaluations for global search
`bloq_method`	`drop`, `m3`	`drop`	How to handle rows with `CENS=1`. `m3` enables Beal's M3 likelihood (see BLOQ example).

Estimation Methods

FOCE (default)

method = foce

First-Order Conditional Estimation. Linearizes the model around the empirical Bayes estimates. Fast and reliable for most models.

FOCEI

method = focei

FOCE with Interaction. Includes the dependence of the residual error on random effects. More accurate than FOCE when the error model depends on individual predictions, but slightly slower.

SAEM

method = saem

Stochastic Approximation EM. Uses Metropolis-Hastings sampling instead of MAP optimization for random effects. More robust to local minima; recommended for complex models with many random effects.

SAEM-Specific Options

Key	Default	Description
`n_exploration`	`150`	Phase 1 iterations (step size = 1)
`n_convergence`	`250`	Phase 2 iterations (step size = 1/k)
`n_mh_steps`	`3`	Metropolis-Hastings steps per subject per iteration
`adapt_interval`	`50`	Iterations between MH step-size adaptation
`seed`	`12345`	RNG seed for reproducibility

SIR (Sampling Importance Resampling)

SIR provides non-parametric parameter uncertainty estimates as an optional post-estimation step. Requires covariance = true.

Key	Default	Description
`sir`	`false`	Enable SIR uncertainty estimation
`sir_samples`	`1000`	Number of proposal samples (M)
`sir_resamples`	`250`	Number of resampled vectors (m)
`sir_seed`	`12345`	RNG seed for reproducibility

See SIR documentation for details.

Optimizer Choices

Optimizer	Description	Recommended For
`slsqp`	Sequential Least Squares Programming (NLopt)	General use (default)
`bfgs`	Built-in BFGS quasi-Newton	When NLopt is unavailable
`lbfgs`	Limited-memory BFGS	Large parameter spaces
`nlopt_lbfgs`	NLopt L-BFGS	Alternative L-BFGS
`mma`	Method of Moving Asymptotes (NLopt)	Constrained problems

Global Search

Setting global_search = true runs a gradient-free pre-search (NLopt CRS2-LM) before the local optimizer. This helps escape local minima on challenging datasets.

The number of global evaluations is auto-scaled based on the number of parameters and observations, or can be set explicitly with global_maxeval.

Examples

Standard FOCE with defaults:

[fit_options]
  method     = foce
  maxiter    = 300
  covariance = true

FOCEI with global search:

[fit_options]
  method        = focei
  maxiter       = 500
  covariance    = true
  global_search = true

SAEM with custom settings:

[fit_options]
  method        = saem
  n_exploration = 200
  n_convergence = 300
  n_mh_steps    = 5
  seed          = 42
  covariance    = true

FOCEI with SIR uncertainty:

[fit_options]
  method        = focei
  covariance    = true
  sir           = true
  sir_samples   = 1000
  sir_resamples = 250
  sir_seed      = 42

FeRx-NLME Documentation